在深度强化学习中转移学习:一项调查
概括
本调查探讨了深度强化学习 (DRL) 中的转移学习 (TL). 我们对最先进的TL方法进行分类,以提高DRL在复杂任务中的效率和有效性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 强化学习 (RL) 是顺序决策的一个关键范式.
- 深度神经网络已经显著提升了RL的能力.
- 在RL效率和有效性方面的挑战需要知识转移.
研究的目的:
- 系统地调查最近转移学习对深度强化学习的进展.
- 为分类最先进的转移学习方法提供一个框架.
- 分析DRL中转移学习的目标,方法,骨干和应用.
主要方法:
- 文献审查和转移学习技术的系统分类.
- 基于定义的标准进行转移学习方法的分析.
- 探索与相关RL主题和未来挑战的联系.
主要成果:
- 一个全面的框架,以了解转移学习在DRL.
- 各种转移学习方法及其应用的分类.
- 确定当前的局限性和未来的研究方向.
结论:
- 转移学习对于推进深度强化学习至关重要.
- 需要进一步的研究来应对挑战并释放充分的潜力.
- 这项调查为DRL的研究人员和从业人员提供了宝贵的资源.
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